Traditional meter data management (MDM) systems employ a centralized processing model, in which raw data from one or more utility meters, sensors, and/or control devices (collectively “utility data collection devices”) is sent to a meter data management system at a central office of a utility provider for processing. As the size and number of customers being serviced by the utility increases, so too do the processing and storage demands on the centralized MDM systems. These demands are likely to increase even more as new and different devices are added to the utility network to support the smart grid and all of the services that the smart grid enables. To meet these increased demands using the centralized MDM systems currently in place, utilities will have to make substantial investments in infrastructure to increase the computing power and storage capacity of the centralized MDM systems. These costs and complexities are multiplied by the need to provide redundancy and fault tolerance of utility network systems.
Furthermore, in today's information age, customers and partners expect data to be available immediately. Currently, however, raw meter data must be processed at the centralized MDM systems before being used for rate calculations, customer billing, customer feedback, and other end uses. Moreover, the data transmission times to and from the centralized MDM and the serialized processing of the data at the centralized MDM introduce data latency that precludes many real time uses of the processed data. The fact that the MDM systems are, by nature, designed to aggregate data and process the aggregated data in batches, only serves to exacerbate the latency and other problems with centralized MDM systems noted above.